Objectives: Maternal protein malnutrition is associated with impaired fetal growth, and lifetime consequences for the offspring. Our group has previously developed a model of protein-restriction in the non-human primate, which was associated with fetal growth restriction, stillbirth, decreased placental perfusion, and evidence of fetal hypoxia, suggesting perturbed vascular development. Previous assessments of placental vasculature have relied upon stereological or vascular casting methods, but these methods have limitations. Our objective was to histologically characterize the micro-anatomic alterations associated with adverse pregnancy outcomes using a newer method that permits investigation of the 3D vascular structure and surrounding histology.

Methods: Rhesus macaques were assigned in the pre-gestational period to a control diet that contained 26% protein, or study diet containing 13% protein (50% PR diet). Placental tissue was collected at delivery and processed using a clarification, immunohistochemistry, and confocal microscopy protocol published previously by our group. 3-dimensional reconstructions and quantitative analysis of the vascular micro-anatomy was performed using analysis software (Imaris®) and statistical analysis incorporated maternal, pregnancy, and perinatal outcomes.

Results: In unadjusted analysis, when comparing those pregnancies on a 50% PR diet (n=4) with those on a control diet (n=4), protein-restriction diet was associated with decreased maternal pre-pregnancy weight (difference of -1.975kg, 95% CI -3.267 to -0.6826).  When controlling for maternal pre-pregnancy weight, fetal sex, and latency from tissue collection to imaging, a gestational protein-restriction diet was associated with decreases in total vascular length, total vascular surface area, total vascular volume, and vascular density.

Conclusion: In this pilot study, a gestational protein-restriction diet was associated with changes in the placental micro-vasculature, which may be related to the observed adverse pregnancy outcomes and perturbed placental perfusion demonstrated in this model.

Files were uploaded to Imaris® 9.2.1 software (Bitplane AG, Zurich, Switzerland) and converted to .ims format. All data acquisition occurred on a custom-built Dell Precision Tower 7910 workstation with an Intel Xeon CPU E5-2623 v4 2.60GHz dual processor with 192GB RAM. Once uploaded into Imaris®, z-stacks were cropped to isolate the area of interest. Pre-processing steps were minimized to prevent loss or alteration of data: a linear stretch was performed to utilize the entire voxel intensity histogram (0 to 65,535), and a background subtraction with a large filter width.

Using the filament module, a pipeline was created to minimize processing parameters between tissues. A single starting point was selected subjectively for each tissue, and a seed point minimum of 5.00µm was set based upon the size of the smallest structure of interest (i.e. capillary diameter). Within the filamentation module of Imaris®, the “no loop” algorithm was used, and the variables of interest (total vascular length, total vascular surface area, total vascular volume, total number of termini, median branching angle, sholl Intersections) were collected and exported for analysis. A full description of the measured and calculated variables of interest are included in the Supplemental. Maternal and pregnancy data was compiled including: maternal age at delivery, maternal pre-pregnancy weight, maternal gestational weight gain, maternal weight at delivery, fetal sex, fetal weight, placenta weight, fetal:placental weight ratio, gestational age at delivery, and latency from tissue collection to imaging.

Statistical Analysis

Statistical analysis was completed using GraphPad Prism version 8.2.0. Descriptive statistics (compared using student’s t-test, with significance threshold of p < 0.01), histograms, correlations (compared using Pearson’s correlation with a threshold p<0.01), and univariate linear regression methods were utilized to assess relationships between variables of interest. A multiple linear regression model was generated to isolate the effect of group assignment on when controlling for multiple covariates chosen based on either a detected significance with the univariate analysis or based on previous studies.

None.